• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.87-100
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• 1995

In order to control systems which contain nonlinearities of uncertainties, control strategies must deal with the effects of them. Since most of control methods based on system mathematical models have been mainly developed focused on stability robustness against nonlinearities or uncertainties under the assumption that controlled systems are linear time invariant, they have certain amount of limitations to smartly improve the transient responses of systems disturbed by nonlinearities or uncertainties. In this paper, a nonlinear fuzzy PID control method is suggested which can stably improve the transient responses of systems disturbed by nonlinearities, as well as systems whose mathematical characteristics are not perfectly known. Although the derivation process is based on the design process similar to general fuzzy logic controller, resultant control law has analytical forms with time varying PID gains rather than linguistic forms, so that implementation using common-used versatile microprocessors cna be achieved easily and effectively in real-time control aspect.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.35-41
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• 2000

This paper describes the design of fuzzy digital PID controller using a simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous-time linear digital PID controller,. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIIM is applied the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated that the proposed method provides better control performance than the one proposed by D. Misir et al.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.169-177
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• 2024

Interacting Spherical tank has maximum storage capacity is broadly utilized in industries because of its high storage capacity. This two tank level system has the nonlinear characteristics due to its varying surface area of cross section of tank. The challenging tasks in industries is to manage the flow rate of liquid. This proposed work plays a major role in controlling the liquid level in avoidance of time delay and error. Several researchers studied and investigated about reducing the nonlinearity problem and their approaches do not provide better result. Different types of controllers with various techniques are implemented by the proposed system. Intelligent Adaptive Neuro Fuzzy Inference System (ANFIS) based Sliding Mode Controller (SMC) with Fractional order PID controller is a novel technique which is developed for a liquid level control in a interacting spherical tank system to avoid the external disturbances perform better result in terms of rise time, settling time and overshoot reduction. The performance of the proposed system is obtained by analyzing the simulation result obtained from the controller. The simulation results are obtained with the help of FOMCON toolbox with MATLAB 2018. Finally, the performance of the conventional controller (FOPID, PID-SMC) and proposed ANFIS based SMC-FOPID controllers are compared and analyzed the performance indices.

• Journal of Industrial Convergence
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• v.16 no.2
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• pp.33-39
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• 2018

This paper describes the design method of the linear PID controller and proposed the design method in the future. The first PID design method is to ensure phase margin and gain margin. This method guarantees stability by designing in the frequency domain. The second method is an internal model control method. This method is to design the PID controller using the parameters of the internal model after identifying the internal model for the control model. Therefore, this method has a strong disturbance characteristic. Finally, a proposed Cascade and smith-Predictor controller. The combination of the cascade controller and the smith-predator of this method is a controller structure that has two advantages: robust control and optimal control. This method can obtain the performance evaluation index as the optimal controller design method. This PID controller design method becomes the basis of the nonlinear method and is being continuously studied.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.155-162
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance, FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and to increase efficiency. a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy PI and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and the resultant rule base is Macvicar-Whelan type. And the membership function is a Gaussian function. The frequency response information is used in tuning of the membership functions. Also a tuning strategy for the scaling factors is proposed based on the relationship between PID gain and the scaling factors. Simulation results show better performance and the effectiveness of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.69-77
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• 1999

This paper describes the design of fuzzy digital PID controller using simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous time linear digital PID controller. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy digital controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional digital PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.105-111
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• 2006

This paper describes a Neural Network based PID control scheme for pneumatic NC axes. Pneumatic systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. A self-excited oscillation method is applied to derive the dynamic design parameters of linear model. The gains of PID controller are determined using a self tuning scheme. The experiments of a trajectory tracking control using the proposed control scheme are performed and a significant reduction in tracking error is achieved by comparing with those of a PID control.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.633-641
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• 2015

Several complex processes that are employed in industries, such as shipping, power plants, and the petrochemical industry, involve time-varying behavior as well as strong nonlinear behavior during operation. The fixed-parameter proportional-integral-derivative (PID) controllers have difficulty in dealing with control problems that occur in such processes. In this paper, we propose a method of designing a nonlinear PID controller for industrial processes that exhibit a large number of nonlinearities and time-varying behavior. The gains of the nonlinear PID controller are characterized by a simple nonlinear function of the error and/or error rate depending on the process set-point and output. We tune the user-defined parameters using a genetic algorithm by minimizing the integral of time absolute error (ITAE) index. We verify the effectiveness of the proposed method by performing a comparison of the proposed method and two other nonlinear and adaptive methods that are employed for reference tracking, disturbance-rejection performances, and robustness to parameter changes on a continuously stirred tank reactor.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2146-2148
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• 2002

In this paper, PID control method using genetic algorithm to control the nonlinear multivariable system is presented. Genetic algorithms are global search techniques for nonlinear optimization. For experiment, the x-y rod balancing system with driver circuit board is fabricated. Experiments such as angle and position control for system are performed. The validity and control performance of the GA-based PID controller are confirmed by experimental results.